Improved sequence variant analysis strategy by automated false positive removal
| Autor: | Wenzhou Li, Jette Wypych, Robert J. Duff |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Computer science 010401 analytical chemistry Immunology Therapeutic protein false positive removal computer.software_genre sequence variant analysis 01 natural sciences 0104 chemical sciences Algorithm Reduction (complexity) 03 medical and health sciences 030104 developmental biology Report False positive paradox Immunology and Allergy Data verification Data mining Perl computer True positive rate automation Sequence (medicine) computer.programming_language |
| Zdroj: | mAbs |
| ISSN: | 1942-0870 1942-0862 |
| DOI: | 10.1080/19420862.2017.1336591 |
| Popis: | Sequence variant analysis (SVA) is critical in therapeutic protein development because it ensures the absence of genetic mutations of a production clone or high-level misincorporations during cell culture. While software for searching sequence variants from mass spectrometry data are available, effectively distinguishing true positives from a large number of false positives in the reported hits or identifications found in the error tolerant search mode is a challenge. This verification process must be done manually and can take several days or even weeks to accomplish. We report here the use of a Perl-based script to evaluate every identified hit to remove the false positives from the search results of PepFinder™ (also known as MassAnalyzer) based on orthogonal criteria. Our data show that the false positives from PepFinder™ output were reduced ∼4-fold without loss of accuracy in the detection of true identifications, representing a more than 70% reduction in time compared with the manual data verification process. |
| Databáze: | OpenAIRE |
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